Connect Google Analytics to Claude

Multi-Client Google Analytics Health Radar

Using the Catchr MCP, analyze Google Analytics for [List of Client Accounts] over [Reporting Period] against [Comparison Period]. For each account, review Sessions, Conversions, and Engagement rate and break the results down by Session source / medium and Default channel group. Focus on traffic quality and acquisition efficiency. Rank accounts as Healthy, Watch, or Critical using [Client Targets or Historical Baseline], and show the evidence behind each status. Return a client-ready table with the account, main change, supporting fields, business implication, recommended next action, and one sentence for the next client meeting. Separate observed changes from possible explanations, respect the reporting grain, and do not claim causation when Google Analytics only shows correlation.

Client-Ready Google Analytics Performance Brief

Using the Catchr MCP, review Google Analytics for [Client Account] during [Reporting Period] versus [Comparison Period]. Review Engagement rate, Active users, and Total revenue and break the results down by Session campaign and Google Ads campaign. Explain what improved, what declined, and which changes are material for acquisition, landing-page, engagement, event, and conversion performance. Produce an executive summary, three evidence-backed wins, three risks, and a prioritized action plan with owner and review date. Include a compact evidence table that ties every conclusion to the returned fields. State any unavailable field, incompatible reporting level, or small-sample limitation before recommending action, and write the final recap in language an agency account manager can use with the client.

Google Analytics Opportunity and Risk Map

Using the Catchr MCP, compare Google Analytics across [List of Client Accounts] for [Period] using [Materiality Threshold] and [Client-Specific Targets]. For every account, review Total revenue, Return on ad spend, and Sessions per user and break the results down by Campaign and Region. Investigate landing-page engagement and conversion gaps and device, market, event, and channel differences. Build an opportunity-and-risk map ranked by business relevance, confidence, and urgency. For each finding, show the account, observed change, supporting fields, plausible explanations, first validation step, and recommended client action. Keep each client separate before creating a portfolio summary, and avoid applying one account's baseline, currency, attribution rule, or reporting definition to another.

Morning Google Analytics Priority Queue

Using the Catchr MCP, pull Google Analytics for [List of Client Accounts] over [Recent Period] and compare it with [Baseline Period]. For each client, review Sessions per user, Engaged sessions, and 1-day active users and break the results down by Session default channel group and First user default channel group. Flag changes beyond [Alert Threshold], separate high-value issues from low-volume noise, and rank the work I should handle today. Return a queue with the client, issue, exact evidence, likely impact stated cautiously, first validation step, recommended action, and a short update I can send to the client. Do not merge accounts until each result has been checked against its own target, currency, timezone, and reporting grain.

Monthly Google Analytics Client Report

Using the Catchr MCP, create a monthly Google Analytics report for [Client Account] covering [Current Period] versus [Previous Period]. Review 1-day active users, 28-day active users, and 7-day active users and break the results down by Month of the year and Session source. Explain the most important movement in acquisition, landing-page, engagement, event, and conversion performance, including what stayed stable and what needs attention. Write a plain-English report with an executive summary, a concise evidence table, three wins, three concerns, and three actions for [Next Period]. Attach an owner and success measure to each action. State the latest available date and any missing fields or intervals so the client can distinguish measured facts from assumptions or incomplete coverage.

Google Analytics Optimization Backlog

Using the Catchr MCP, analyze Google Analytics for [Client Account] over [Lookback Period]. Review 7-day active users, Cohort active users, and Session conversion rate and break the results down by Source / medium and Source. Identify the largest opportunities related to traffic quality and acquisition efficiency and device, market, event, and channel differences, then create an optimization backlog ranked by expected impact, effort, and confidence. For every task, include the observed evidence, a specific action, an owner, a due date, a success metric, and a guardrail that would stop the test. Use [Minimum Volume] and [Materiality Threshold] to avoid reacting to noise, and label any recommendation that requires a field or business definition not available in the returned dataset.

Google Analytics Acquisition Quality and Conversion Review

Using the Catchr MCP, analyze Google Analytics for [Account or Entity] over [Reporting Period] versus [Comparison Period]. Review Session conversion rate, User engagement, and User conversion rate and break the results down by Medium and Session campaign ID. Concentrate on traffic quality and acquisition efficiency. Identify the entities and periods driving the largest change, distinguish material movement from normal variation using [Minimum Volume] and [Business Target], and explain what the available fields can and cannot prove. Return a ranked findings table with evidence, business implication, confidence, and recommended next action, followed by a short decision summary. Keep calculations at a compatible reporting grain and do not invent a metric when Google Analytics exposes only a related signal.

Google Analytics Landing Page Opportunity Map

Using the Catchr MCP, investigate Google Analytics for [Account or Entity] across [Period] with a focus on landing-page engagement and conversion gaps. Review User conversion rate, Average purchase revenue per user, and Events per session and break the results down by Session Google Ads campaign ID and Session Google Ads campaign. Compare relevant entities against [Target, Peer Group, or Historical Baseline], then locate the strongest opportunities and clearest deterioration. For every finding, show the returned values, comparison dimension, possible explanations, and next check required before action. Deliver a prioritized opportunity map with owner, action, expected signal of improvement, and review date. Treat associations as hypotheses unless the available Google Analytics fields establish a direct relationship.

Google Analytics Channel and Device Behavior Analysis

Using the Catchr MCP, review Google Analytics for [Account or Entity] over [Lookback Period] to assess device, market, event, and channel differences. Review Events per session, Event count per user, and Views per session and break the results down by Session Google Ads campaign type and First user Google Ads campaign ID. Flag changes outside [Tolerance], rank them by materiality and confidence, and separate confirmed data observations from possible operational causes. Return an exception table with the affected entity, period, supporting fields, severity, owner, first validation step, and recommended response. End with three repeatable monitoring rules, including the threshold, minimum volume, and required field. Do not present an unusual value as proof of an error or business cause.

Google Analytics Data Quality Audit

Using the Catchr MCP, audit Google Analytics for [Account or All Connected Accounts] over [Period]. Review Views per session, Views per user, and Average session duration and break the results down by First user Google Ads campaign and First user Google Ads campaign type. Check completeness, unexpected nulls, duplicate identifiers at [Expected Grain], invalid values, incompatible aggregation levels, and date consistency where the returned fields support those tests. Return rule-level failure counts and a severity-ranked exception sample with account, field, observed value, expected condition, and reproducible validation step. Distinguish source-data issues from extraction or modeling issues, and do not infer uniqueness, join keys, or business meaning unless confirmed in [Data Contract or Source Documentation].

Google Analytics Freshness and Coverage Monitor

Using the Catchr MCP, monitor Google Analytics delivery for [Account or All Connected Accounts] across [Lookback Period]. Review Average session duration, Crash-affected users, and Crash-free users rate and break the results down by Google Ads campaign ID and Google Ads campaign type. Measure date coverage, latest available record, row or entity volume, missing intervals, and field population against [Expected Schedule] and [Freshness SLA]. Flag stale extraction, flatlined volume, schema coverage changes, and gaps beyond [Absolute and Relative Thresholds]. Return an account-level health summary plus an investigation table with first affected period, supporting fields, likely failure domain stated cautiously, owner, and next validation query. Separate genuine source inactivity from a pipeline issue whenever the evidence cannot distinguish them.

Google Analytics Drift and Reconciliation Review

Using the Catchr MCP, compare Google Analytics across [Current Window] and [Baseline Window] for [Account or All Connected Accounts]. Review Crash-free users rate, New users, and Scrolled users and break the results down by First user source / medium and Month. Profile distributions, null rates, distinct-value counts, volume, and material changes at the declared reporting grain. Reconcile related fields only when their definitions and aggregation levels are compatible. Flag abrupt shifts beyond [Drift Threshold], inconsistent totals, and values that violate [Business or Data Contract Rules]. Produce a reproducible alert table with evidence, severity, probable domain, and next check, followed by a concise assessment of which analyses may be unreliable until each issue is resolved.

Replace [variables] Facts ≠ hypotheses Action + follow-up metric

Connect Google Analytics to Claude

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How to connect Google Analytics to Claude ?

Authorize the Google Analytics account in Catchr

Add the Catchr MCP as a custom connector in Claude

Choose, adapt, and run a prompt.

A simple client question should not trigger an hour of data prep.

Pull another CSV

Rebuild the context

Summarize it by hand

Ask the account. Get to the explanation.

Prepare data before every question.

Connect once. Keep asking.

Twelve Google Analytics prompts for analyzing real account data.

Google Analytics to Claude FAQs

What Google Analytics data can Claude analyze through Catchr?

How granular can Google Analytics analysis be in Claude?

Can Claude compare Google Analytics performance across periods or accounts?

Can Claude investigate unusual changes in Google Analytics?

What types of prompts work best for Google Analytics analysis in Claude?

Can I combine Google Analytics with other data sources in Claude?

Can Claude compare multiple Google Analytics accounts or entities?

What do I need to connect Google Analytics to Claude with Catchr?

How recent is the Google Analytics data, and how much history can Claude analyze?

Can Claude change anything in Google Analytics through Catchr?

How much does it cost to connect marketing data to Claude with Catchr?

Can I try the Claude integration before choosing a plan?

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